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Cited by 998Computational Drug Discovery MethodsSynthesis and Characterization of Heterocyclic CompoundsChemical Synthesis and Analysis

Ultra-large library docking for discovering new chemotypes

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Lyu, Jiankun, Wang, Sheng, Balius, Trent E., Singh, Isha, Levit, Anat, Moroz, Yurii S., O’Meara, Matthew J., Che, Tao +6 more·2019·Nature
DOI 10.1038/s41586-019-0917-9PMC PMC6383769
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Abstract

Despite intense interest in expanding chemical space, libraries containing hundreds-of-millions to billions of diverse molecules have remained inaccessible. Here we investigate structure-based docking of 170 million make-on-demand compounds from 130 well-characterized reactions. The resulting library is diverse, representing over 10.7 million scaffolds that are otherwise unavailable. For each compound in the library, docking against AmpC β-lactamase (AmpC) and the D 4 dopamine receptor were simulated. From the top-ranking molecules, 44 and 549 compounds were synthesized and tested for interactions with AmpC and the D 4 dopamine receptor, respectively. We found a phenolate inhibitor of AmpC, which revealed a group of inhibitors without known precedent. This molecule was optimized to 77 nM, which places it among the most potent non-covalent AmpC inhibitors known. Crystal structures of this and other AmpC inhibitors confirmed the docking predictions. Against the D 4 dopamine receptor, hit rates fell almost monotonically with docking score, and a hit-rate versus score curve predicted that the library contained 453,000 ligands for the D 4 dopamine receptor. Of 81 new chemotypes discovered, 30 showed submicromolar activity, including a 180-pM subtype-selective agonist of the D 4 dopamine receptor.

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  • The experimental data available for training molecular property prediction models is limited compared to the extremely diverse and combinatorially large universe of candidate molecules.

    “However, the experimental data available for training such models is limited compared to the extremely diverse and combinatorially large universe of candidate molecules that we would want to make predictions on (Bohacek et al., 1996; Sterling and Irwin, 2015; Lyu et al., 2019; McCloskey et al., 2020).”

    From WILDS: A Benchmark of in-the-Wild Distribution Shifts (2020) · cites this paper as Lyu et al., 2019

    The Abstract states that possible drug-like molecules vastly outnumber those in early discovery libraries, and reports testing only a small selection from a 170-million-compound virtual library. This supports the scale contrast, but the paper does not establish that experimental data for training molecular property prediction models is limited. (LLM, conf=0.84)

    Experimental — accuracy not yet measured

  • The experimental data available for training models to predict biochemical properties is limited compared to the extremely diverse and combinatorially large universe of candidate molecules.

    “However, the experimental data available for training such models is limited compared to the extremely diverse and combinatorially large universe of candidate molecules that we would want to make predictions on”

    From WILDS: A Benchmark of in-the-Wild Distribution Shifts (2020) · cites this paper as Lyu et al., 2019

    The Abstract (page not provided) says possible drug-like molecules vastly outnumber those in early discovery libraries, and the Expanding chemical space section describes a large, diverse virtual library. The paper does not establish that experimental data for training biochemical-property prediction models is limited relative to that universe. (LLM, conf=0.84)

    Experimental — accuracy not yet measured

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Research works printed but not linked: 67

  1. [1]Bohacek RS, McMartin C & Guida WC The art and practice of structure-based drug design: A molecular modeling perspective. Medicinal Research Reviews 16, 3– 50, (1996). 8788213
  2. [2]Ertl P Cheminformatics Analysis of Organic Substituents: Identification of the Most Common Substituents, Calculation of Substituent Properties, and Automatic Identification of Drug-like Bioisosteric Groups. Journal of Chemical Information and Computer Sciences 43, 374– 380, (2003). 12653499
  3. [3]Fink T, Bruggesser H & Reymond JL Virtual Exploration of the Small-Molecule Chemical Universe below 160 Daltons. Angewandte Chemie International Edition 44, 1504– 1508, (2005). 15674983
  4. [4]Chevillard F & Kolb P SCUBIDOO: a large yet screenable and easily searchable database of computationally created chemical compounds optimized toward high likelihood of synthetic tractability. Journal of chemical information and modeling 55, 1824– 1835 (2015). 26282054
  5. [5]Keserü GM & Makara GM The influence of lead discovery strategies on the properties of drug candidates. Nature Reviews Drug Discovery 8, 203, (2009). 19247303
  6. [6]McGovern SL, Caselli E, Grigorieff N & Shoichet BK A Common Mechanism Underlying Promiscuous Inhibitors from Virtual and High-Throughput Screening. Journal of Medicinal Chemistry 45, 1712– 1722, (2002). 11931626
  7. [8]Ahn S Allosteric “beta-blocker” isolated from a DNA-encoded small molecule library. Proceedings of the National Academy of Sciences 114, 1708 (2017). PMC5321036 28130548
  8. [10]Jorgensen WL The many roles of computation in drug discovery. Science 303, 1813– 1818 (2004). 15031495
  9. [11]De Graaf C Crystal structure-based virtual screening for fragment-like ligands of the human histamine H1 receptor. Journal of medicinal chemistry 54, 8195– 8206 (2011). 22007643 PMC3228891
  10. [12]Katritch V Structure-based discovery of novel chemotypes for adenosine A2A receptor antagonists. Journal of medicinal chemistry 53, 1799– 1809 (2010). 20095623 PMC2826142
  11. [13]Manglik A Structure-based discovery of opioid analgesics with reduced side effects. Nature 537, 185 (2016). 27533032 PMC5161585
  12. [14]Wang S D4 dopamine receptor high-resolution structures enable the discovery of selective agonists. Science 358, 381– 386 (2017). 29051383 PMC5856174
  13. [15]Negri A Discovery of a novel selective kappa-opioid receptor agonist using crystal structure-based virtual screening. Journal of chemical information and modeling 53, 521– 526 (2013). 23461591 PMC3702663
  14. [16]Jazayeri A, Andrews SP & Marshall FH Structurally enabled discovery of adenosine A2A receptor antagonists. Chemical reviews 117, 21– 37 (2016). 27333206
  15. [17]Lane JR Structure-based ligand discovery targeting orthosteric and allosteric pockets of dopamine receptors. Molecular pharmacology 84, 794– 807 (2013). 24021214 PMC3834142
  16. [18]Langmead CJ Identification of novel adenosine A2A receptor antagonists by virtual screening. Journal of medicinal chemistry 55, 1904– 1909 (2012). 22250781 PMC3308209
  17. [19]Becker OM G protein-coupled receptors: in silico drug discovery in 3D. Proceedings of the National Academy of Sciences 101, 11304– 11309 (2004). PMC509175 15277683
  18. [20]Kooistra AJ Function-specific virtual screening for GPCR ligands using a combined scoring method. Scientific reports 6, 28288 (2016). 27339552 PMC4919634
  19. [21]Congreve M Discovery of 1, 2, 4-triazine derivatives as adenosine A2A antagonists using structure based drug design. Journal of medicinal chemistry 55, 1898– 1903 (2012). 22220592 PMC3308197
  20. [22]Kiss R Discovery of novel human histamine H4 receptor ligands by large-scale structure-based virtual screening. Journal of medicinal chemistry 51, 3145– 3153 (2008). 18459760
  21. [23]Oprea TI & Gottfries J Chemography: the art of navigating in chemical space. Journal of combinatorial chemistry 3, 157– 166 (2001). 11300855
  22. [25]Bemis GW & Murcko MA The Properties of Known Drugs. 1. Molecular Frameworks. Journal of Medicinal Chemistry 39, 2887– 2893, (1996). 8709122
  23. [26]Gaulton A The ChEMBL database in 2017. Nucleic acids research 45, D945– D954 (2016). 27899562 PMC5210557
  24. [27]Katz BA A novel serine protease inhibition motif involving a multi-centered short hydrogen bonding network at the active site1. Journal of molecular biology 307, 1451– 1486 (2001). 11292354
  25. [28]Congreve M, Langmead CJ, Mason JS & Marshall FH Progress in structure based drug design for G protein-coupled receptors. Journal of medicinal chemistry 54, 4283– 4311 (2011). 21615150 PMC3308205
  26. [29]Vaidehi N Dynamics and flexibility of G-protein-coupled receptor conformations and their relevance to drug design. Drug discovery today 15, 951– 957 (2010). 20831898
  27. [30]Irwin JJ & Shoichet BK Docking Screens for Novel Ligands Conferring New Biology. Journal of Medicinal Chemistry 59, 4103– 4120, (2016). 26913380 PMC4865415
  28. [31]Vass M in Computational Methods for GPCR Drug Discovery 73– 113 (Springer, 2018).
  29. [32]Isberg V Generic GPCR residue numbers–aligning topology maps while minding the gaps. Trends in pharmacological sciences 36, 22– 31 (2015). 25541108 PMC4408928
  30. [33]McCorvy JD Structural determinants of 5-HT2B receptor activation and biased agonism. Nature Structural & Molecular Biology 25, 787– 796, (2018). PMC6237183 30127358
  31. [35]Powers RA, Morandi F & Shoichet BK Structure-based discovery of a novel, noncovalent inhibitor of AmpC β-lactamase. Structure 10, 1013– 1023 (2002). 12121656
  32. [36]Feng BY A High-Throughput Screen for Aggregation-Based Inhibition in a Large Compound Library. Journal of Medicinal Chemistry 50, 2385– 2390, (2007). 17447748
  33. [37]Babaoglu K Comprehensive Mechanistic Analysis of Hits from High-Throughput and Docking Screens against β-Lactamase. Journal of Medicinal Chemistry 51, 2502– 2511, (2008). 18333608 PMC2655312
  34. [38]Rowley M 5-(4-Chlorophenyl)-4-methyl-3-(1-(2-phenylethyl) piperidin-4-yl) isoxazole: a potent, selective antagonist at human cloned dopamine D4 receptors. Journal of medicinal chemistry 39, 1943– 1945 (1996). 8642551
  35. [39]Enguehard-Gueiffier C 2-[(4-phenylpiperazin-1-yl) methyl] imidazo (di) azines as selective D4-ligands. Induction of penile erection by 2-[4-(2-methoxyphenyl) piperazin-1-ylmethyl] imidazo [1, 2-a] pyridine (PIP3EA), a potent and selective D4 partial agonist. Journal of medicinal chemistry 49, 3938– 3947 (2006). 16789750
  36. [40]Löber S, Hübner H & Gmeiner P Synthesis and biological investigations of dopaminergic partial agonists preferentially recognizing the D4 receptor subtype. Bioorganic & medicinal chemistry letters 16, 2955– 2959 (2006). 16563764
  37. [41]Lindsley CW & Hopkins CR Return of D4 Dopamine Receptor Antagonists in Drug Discovery: Miniperspective. Journal of medicinal chemistry 60, 7233– 7243 (2017). 28489950
  38. [42]Tirado-Rives J & Jorgensen WL Contribution of conformer focusing to the uncertainty in predicting free energies for protein− ligand binding. Journal of medicinal chemistry 49, 5880– 5884 (2006). 17004703
  39. [43]Abagyan R, Totrov M & Kuznetsov D ICM—a new method for protein modeling and design: applications to docking and structure prediction from the distorted native conformation. Journal of computational chemistry 15, 488– 506 (1994).
  40. [44]Halgren TA Glide: a new approach for rapid, accurate docking and scoring. 2. Enrichment factors in database screening. Journal of medicinal chemistry 47, 1750– 1759 (2004). 15027866
  41. [45]Goodsell DS & Olson AJ Automated docking of substrates to proteins by simulated annealing. Proteins: Structure, Function, and Bioinformatics 8, 195– 202 (1990). 2281083
  42. [46]Kufareva I, Katritch V, Stevens RC & Abagyan R Advances in GPCR modeling evaluated by the GPCR Dock 2013 assessment: meeting new challenges. Structure 22, 1120– 1139 (2014). 25066135 PMC4126895
  43. [47]Kramer B, Rarey M & Lengauer T Evaluation of the FLEXX incremental construction algorithm for protein–ligand docking. Proteins: Structure, Function, and Bioinformatics 37, 228– 241 (1999). 10584068
  44. [48]McGann M FRED pose prediction and virtual screening accuracy. Journal of chemical information and modeling 51, 578– 596 (2011). 21323318
  45. [49]Jones G, Willett P, Glen RC, Leach AR & Taylor R Development and validation of a genetic algorithm for flexible docking. Journal of molecular biology 267, 727– 748 (1997). 9126849
  46. [50]Corbeil CR, Williams CI & Labute P Variability in docking success rates due to dataset preparation. Journal of computer-aided molecular design 26, 775– 786 (2012). 22566074 PMC3397132
  47. [51]Hawkins PC, Skillman AG, Warren GL, Ellingson BA & Stahl MT Conformer generation with OMEGA: algorithm and validation using high quality structures from the Protein Databank and Cambridge Structural Database. Journal of chemical information and modeling 50, 572– 584 (2010). 20235588 PMC2859685
  48. [54]Mysinger MM & Shoichet BK Rapid context-dependent ligand desolvation in molecular docking. Journal of chemical information and modeling 50, 1561– 1573 (2010). 20735049
  49. [55]Sterling T & Irwin JJ ZINC 15 – Ligand Discovery for Everyone. Journal of Chemical Information and Modeling 55, 2324– 2337, (2015). 26479676 PMC4658288
  50. [56]Barelier S Increasing chemical space coverage by combining empirical and computational fragment screens. ACS chemical biology 9, 1528– 1535 (2014). 24807704 PMC4215856
  51. [57]Gray DL Impaired β-arrestin recruitment and reduced desensitization by non-catechol agonists of the D1 dopamine receptor. Nature communications 9, 674 (2018). PMC5813016 29445200
  52. [60]Carlsson J Ligand discovery from a dopamine D3 receptor homology model and crystal structure. Nature chemical biology 7, 769– 778 (2011). 21926995 PMC3197762
  53. [61]Meng EC, Shoichet BK & Kuntz ID Automated docking with grid-based energy evaluation. Journal of computational chemistry 13, 505– 524 (1992).
  54. [62]Sharp KA, Friedman RA, Misra V, Hecht J & Honig B Salt effects on polyelectrolyte–ligand binding: Comparison of Poisson–Boltzmann, and limiting law/counterion binding models. Biopolymers 36, 245– 262 (1995). 7492748
  55. [63]Gallagher K & Sharp K Electrostatic contributions to heat capacity changes of DNA-ligand binding. Biophysical journal 75, 769– 776 (1998). 9675178 PMC1299751
  56. [64]Coleman RG, Carchia M, Sterling T, Irwin JJ & Shoichet BK Ligand pose and orientational sampling in molecular docking. PloS one 8, e75992 (2013). 24098414 PMC3787967
  57. [65]Tolmachev A Expanding Synthesizable Space of Disubstituted 1, 2, 4-Oxadiazoles. ACS combinatorial science 18, 616– 624 (2016). 27548754
  58. [66]Eidam O Design, synthesis, crystal structures, and antimicrobial activity of sulfonamide boronic acids as β-lactamase inhibitors. Journal of medicinal chemistry 53, 7852– 7863 (2010). 20945905 PMC3166525
  59. [67]Kabsch WXDS. Acta Crystallographica Section D: Biological Crystallography 66, 125– 132, (2010). 20124692 PMC2815665
  60. [68]Emsley P, Lohkamp B, Scott WG & Cowtan K Features and development of Coot. Acta Crystallographica Section D: Biological Crystallography 66, 486– 501 (2010). 20383002 PMC2852313
  61. [69]Murshudov GN, Vagin AA & Dodson EJ Refinement of macromolecular structures by the maximum-likelihood method. Acta Crystallographica Section D: Biological Crystallography 53, 240– 255 (1997). 15299926
  62. [70]Adams PD PHENIX: a comprehensive Python-based system for macromolecular structure solution. Acta Crystallographica Section D: Biological Crystallography 66, 213– 221 (2010). 20124702 PMC2815670
  63. [71]Eidam O Fragment-guided design of subnanomolar β-lactamase inhibitors active in vivo. Proceedings of the National Academy of Sciences 109, 17448– 17453 (2012). PMC3491531 23043117
  64. [72]Feng BY & Shoichet BK A detergent-based assay for the detection of promiscuous inhibitors. Nature protocols 1, 550 (2006). 17191086 PMC1544377
  65. [73]Allen JA Discovery of β-arrestin–biased dopamine D2 ligands for probing signal transduction pathways essential for antipsychotic efficacy. Proceedings of the National Academy of Sciences 108, 18488– 18493 (2011). PMC3215024 22025698
  66. [75]R. EG, D. CC, M. JM & P. AN A Review of Modern Computational Algorithms for Bayesian Optimal Design. International Statistical Review 84, 128– 154, (2016).
  67. [76]Rainforth T, Cornish R, Yang H, Warrington A & Wood F On Nesting Monte Carlo Estimators. To appear at International Conference on Machine Learning 2018, (2018).

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Authors
Lyu, J., Wang, S., Balius, T. E., Singh, I., Levit, A., Moroz, Y. S., O’Meara, M. J., Che, T., Algaa, E., Tolmachova, K., Tolmachev, A. A., Shoichet, B. K., Roth, B. L., & Irwin, J. J.
Affiliations
  • East China University of Science and Technology (CN)
  • University of North Carolina at Chapel Hill (US)
  • University of California, San Francisco (US)
  • Taras Shevchenko National University of Kyiv (UA)
  • Enamine (Ukraine) (UA)
Corresponding author
  • Shoichet, Brian K.
  • Roth, Bryan L.
  • Irwin, John J.
ORCID
Venue
Nature
Publisher
Springer Science and Business Media LLC
Published
2019-02-06
Volume
566
Issue
7743
Pages
224-229
ISSN
  • 0028-0836 (ISSN-L)
  • 1476-4687
DOI
10.1038/s41586-019-0917-9
PMC
PMC6383769
OpenAlex
W2912171584
Cited by
998
References
6
Citations by year
2026: 127 · 2025: 131 · 2024: 162 · 2023: 148 · 2022: 116 · 2021: 141 · 2020: 113 · 2019: 60
FWCI
75.34
Type
journal article
Language
en
Open access
Licence
Locations
  • Nature (publishedVersion)
  • PubMed (publishedVersion)
  • Europe PMC (PubMed Central) (acceptedVersion)
  • PubMed Central (acceptedVersion)
  • Carolina Digital Repository (University of North Carolina at Chapel Hill) (submittedVersion)
  • RePEc: Research Papers in Economics (submittedVersion)
  • eScholarship (California Digital Library) (submittedVersion)
  • UNC Libraries
Funders
  • National Institute of Mental Health (R01MH112205)
  • National Cancer Institute (P30CA016086)
  • National Institute of General Medical Sciences (R01GM071896, R35GM122481)
  • National Institute of Diabetes and Digestive and Kidney Diseases (U24DK116195)
  • National Institute of Neurological Disorders and Stroke (F31NS093917)
Topics
  • Physical Sciences › Computer Science › Computational Theory and Mathematics › Computational Drug Discovery Methods
  • Physical Sciences › Chemistry › Organic Chemistry › Synthesis and Characterization of Heterocyclic Compounds
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Chemical Synthesis and Analysis
Keywords
structure-based virtual screening, dopamine D4 receptor, chemical space expansion, non-covalent inhibitors, subtype-selective agonists
MeSH terms
Machine Learning, Bacterial Proteins, beta-Lactamases, Humans, Ligands, Crystallography, X-Ray, Dopamine Agonists, Observation, Receptors, Dopamine D4, Small Molecule Libraries, Molecular Docking Simulation, beta-Lactamase Inhibitors

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